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J^ 8859 



Bureau of Mines Information Circular/1981 



An Introduction to the Mine Inspection 
Data Analysis System (MIDAS) 



By W. F. Watts, Jr., R. L. Johnson, D. J. Donaven, 
and D. R. Parker 




UNITED STATES DEPARTMENT OF THE INTERIOR 



Information Circular 8859 

An Introduction to the Mine Inspection 
Data Analysis System (MIDAS) 



By W. F. Watts, Jr., R. L. Johnson, D. J. Donaven, 
and D. R. Parker 




UNITED STATES DEPARTMENT OF THE INTERIOR 
James G. Watt, Secretary 

BUREAU OF MINES 
Robert C. Horton, Director 




This publication has been cataloged as follows: 



fH 



a^ 



IM,?^^ 



fP- 



i 



An introduction to the mine inspection data analysis system 
(MIDAS), 

(Information circular/United States. Bureau of Mines ; 8859« ) 

Includes bibliographies. 

Supt. of Docs, no.: I 28.27:8859. 

1. MIDAS (Computer system). I. Watts, W. F. (Winthrop F.). II. 
Series. 

TN295.U4 [TN295] 622s [622'.8'0285425] 81-607306 AACR2 



.«.\ 



CONTENTS 



Page 



Abstract 1 

Introduction 1 

Coding system for sample collection 2 

Approach 6 

Limitations and uses 6 

Data editing 8 

Statistical programs. 10 

Initial results of dust analysis 10 

Summary 19 

Appendix 20 

ILLUSTRATIONS 

1. Log probability plot of the concentration-TLV ratios for MSHA 

data, 1974-80 14 

2. Log probability plot of concentrations for MSHA data, 1974-80 14 

3. Log probability plot for respirable quartz dust for 1975-76 and 

1979 17 

4. Log probability plot for respirable quartz dust concentration-TLV 

ratios for 1975-76 and 1979 17 

5. Log probability plot for total nuisance dust concentration-TLV 

ratios for 1975-76 and 1979 18 

A-1, Probability plot of respirable quartz dust data, 1974-80 20 

A-2. Log normal probability plot of respirable quartz dust data, 

1974-80 21 

A-3. Probability plot of total nuisance dust data, 1974-80 22 

A-4. Log normal probability plot of total nuisance dust data, 1974-80.. 23 

A-5. Probability plot of total silica dust data, 1974-80 24 

A-6. Log normal probability plot of total silica dust data, 1974-80.... 25 

TABLES 

1. Ten most frequently sampled contaminants in metal and nonmetal 

mines, January 1974 through March 1980 3 

2. Respirable quartz dust data from 1974 through 1980 for the 

sandstone industry 4 

3. Sandstone respirable quartz dust data summarized by year 5 

4. Editing rules for MSHA dust data 9 

5. Effect of editing contaminant records 10 

6. Summary statistics for respirable quartz dust, total nuisance 

dust, asbestos fiber, and total silica dust 11 

7. MSHA respirable quartz dust samples for 1978-79 for eight dusty 

operations 12 

8. MSHA nuisance dust samples for 1978-79 for 12 dusty operations.... 13 

9. MSHA total silica dust samples for 1978-79 for nine dusty 

operations 13 

10. Top 20 metal and nonmetal industries by active employment 16 

A-1. MSHA industry codes for metal and nonmetal mines 26 



11 



TABLES — Continued 



Page 



A-2. MSHA mine location codes for metal and nonmetal mines 26 

A-3. MSHA operation codes for metal and nonmetal mines 27 

A-4. MSHA contaminant codes for personal samples and the number of 

records for each contaminant 28 

A-5. MSHA contaminant codes for area samples and the number of records 

for each contaminant 29 

A-6. Industry by location breakdown for respirable quartz dust, 1975.. 30 

A-7. Industry by location breakdown for respirable quartz dust, 1976.. 31 

A-8. Industry by location breakdown for respirable quartz dust, 1977.. 32 

A-9. Industry by location breakdown for respirable quartz dust, 1978.. 33 

A-10. Industry by location breakdown for respirable quartz dust, 1979.. 34 
A-11. Industry by location breakdown for respirable quartz dust, 

1974-80 35 

A-12. Operation by location breakdown for respirable quartz dust, 1975. 36 

A-13. Operation by location breakdown for respirable quartz dust, 1976. 37 

A-14. Operation by location breakdown for respirable quartz dust, 1977. 38 

A-15. Operation by location breakdown for respirable quartz dust, 1978. 39 

A-16. Operation by location breakdown for respirable quartz dust, 1979. 40 
A-17. Operation by location breakdown for respirable quartz dust, 

1974-80 41 



AN INTRODUCTION TO THE MINE INSPECTION DATA ANALYSIS 

SYSTEM (MIDAS) 

by 

W. F. Watts, Jr.,1 R. L. Johnson, 2 D. J. Donaven,3 and D. R. Parl<er4 



ABSTRACT 

This report describes the Mine Inspection Data Analysis System (MIDAS) 
developed by the Bureau of Mines to analyze the records of industrial hygiene 
samples collected by the Mine Safety and Health Administration (MSHA) in metal 
and nonmetal mines. MIDAS is the first system capable of sorting, editing, 
analyzing, and reporting these data. It is also the first system designed to 
be used by a number of Government agencies. At present the system contains 
225,000 personal and area samples for 61 contaminants in 45 industries. The 
records date from 1974 to early 1980, and MSHA plans to provide yearly updates 
to the system. This report presents preliminary analyses of dust exposures 
and discusses the potential uses and limitations of these data. 

Analysis of the dust data for 1978 and 1979 showed that bagging had the 
highest percentage of dust overexposure. More than 40 pet of the 1,536 res- 
pirable quartz dust, total nuisance dust, and total silica dust samples 
exceeded the MSHA exposure limit. Other dusty occupations are ranked accord- 
ing to their percentage of overexposure. 

INTRODUCTION 

The Mine Inspection Data Analysis System (MIDAS) developed by the Bureau 
provides the first systematic, computerized capability to analyze the Mine 
Safety and Health Administration's (MSHA) inspection data from metal and non- 
metal mines and mills. Results from MIDAS are used by the Bureau, MSHA, and 
the National Institute for Occupational Safety and Health (NIOSH) to plan, 
rank, evaluate, and conduct their health programs in metal and nonmetal min- 
ing. Computer programs contained in MIDAS are designed to edit, sort, analyze, 

^Industrial hygienist. Twin Cities Research Center, Bureau of Mines, Twin 

Cities, Minn. 
^Operations research analyst. Division of Automatic Data Processing, Bureau of 

Mines, Denver, Colo. 
^Operations research analyst. Division of Automatic Data Processing, Bureau of 

Mines, Denver, Colo, (now retired). 
'^Program analyst. Mine Safety and Health Administration, U.S. Department of 

Labor, Arlington, Va. 



and report results as summary tables or graphs, and interactive programing 
allows flexibility in the types of analyses. For instance, additive exposures 
are calculated for welders or other miners exposed to more than one contami- 
nant, historical data are analyzed to aid in the selection of mine sites for 
future work, and problem areas are identified from records of overexposure. 
The capability to systematically analyze the environmental industrial hygiene 
data for metal and nonmetal mines did not exist prior to the development of 
MIDAS . 

This report is the first in a series describing MIDAS, its uses, and lim- 
itations. Future reports will analyze the large variety of MSHA sampling rec- 
ords, include periodic updating of the data, and exploit the flexibility of 
the data analysis system. These reports will be organized by contaminant. 
This report covers the necessary background material and serves as a reference 
for future reports. It presents preliminary results from the analysis of 
three major dust contaminants: respirable quartz dust, total nuisance dust, 
and total silica dust. 

In January 1980, the Bureau requested that MSHA make available copies of 
the computer tapes containing sampling data from metal and nonmetal mine 
inspections. These tapes along with file layouts, mine directory information, 
and several COBOL programs were given to the Bureau's data processing center 
in Denver. An update to this information was received in March 1980. These 
data, which date from September 1974 through early 1980 and include a total of 
144,901 personal samples and 80,087 area samples, are the basis of this report. 

CODING SYSTEM FOR SAMPLE COLLECTION 

When MSHA inspectors collect personal or area samples, they are required 
to record additional information about the site; this information and the lab- 
oratory results constitute a complete record. About 225,000 records are now 
stored in MIDAS, and MSHA is adding about 70,000 new records yearly. These 
records contain about 2.5 million bits of information which MIDAS sorts, ana- 
lyzes, and reports using the MSHA code categories. 

A record of personal exposure contains the following information: con- 
taminant, concentration, threshold limit value (TLV),^ work location, work 
operation (occupation), date of collection, mine identification number, per- 
sonal protection being used, inspector's identification number, and the MSHA 
subdistrict. MIDAS adds to every record the industry code. MIDAS calculates 
the concentration-to-TLV ratio (C/TLV) for every record, and for respirable 
quartz dust and total silica dust MIDAS calculates the percent quartz in the 
sample. Tables A-1 through A-4 summarize the main MSHA codes for "industry," 
"location", "operation," and "contaminant" and show the total number of rec- 
ords for each code. 



^MSHA has adopted the threshold limit values set forth by the American 

Conference of Governmental Industrial Hygienists in "TLVs Threshold Limit 
Values for Chemical Substances in Workroom Air Adopted by ACGIH in 1973." 
Cincinnati, Ohio, 1973, pp. 1-54. The sole exception is asbestos fiber. 



Area samples are collected for the contaminants listed in table A-5. 
Each area record contains information on contaminant, concentration, type of 
sample, time sampling started and stopped, area of sample collection, mine and 
inspector identification numbers, and the MSHA subdistrict. Area samples will 
be discussed further in later reports in this series. 

A second source of information in MIDAS is the mine directory, which 
lists all metal and nonmetal mines and mills inspected by MSHA. Information 
in this directory is updated annually by MSHA. Each mine or mill listed has 
an identification number, a company and operation name, a location, the type 
of mine, the status of the mine (either active, intermittent, or closed), the 
travel area, the number of employees and a related size code, the date of last 
regular inspection, the industry, and the State and county. This information 
is most useful when a mine identification number must be associated with a 
name and location, or when a list of all mines in a particular industry or 
region is needed. Unfortunately, data on the number of employees in each 
occupational category are not available. 

The coding system used by MSHA allows MIDAS to analyze and summarize 
information in a variety of ways, and tables 1 through 3 illustrate this flex- 
ibility. Ten contaminants, accounting for 93 pet of the 224,988 samples, are 
listed in table 1. 

TABLE 1 . - Ten most frequently sampled contaminants in metal and nonmetal 

mines, January 1974 through March 1980 



Contaminant 


Number of samples 


Percent of total samples 


Type of sample 


Noise dosimeter.. 

Respirable quartz 

dus t............ 


73,247 

42,865 

17,223 

15,736 

14,582 

13,431 

9,814 

9,435 

7,153 

6,160 


32.6 

19.0 
7.6 
7.0 
6.5 
6.0 
4.4 
4.2 
3.2 
2.7 


Personal. 
Do. 
Do. 


Carbon monoxide.. 
Radon daughter... 
Carbon dioxide... 
Methane .......... 


Area. 
Do. 
Do. 
Do. 


Nuisance dust . . . . 
Oxygen 

Total silica dust 
Nitrogen dioxide. 


Personal. 
Area. 
Personal. 
Area. 


Subtotal. . . . 
Others 


209,646 
15,342 


93.2 
6.8 




Total 


224,988 


100. 





Table 2 summarizes all respirable quartz dust data for the sandstone 
industry. The column headings of underground, open pit, sand and gravel, sur- 
face, and mill correspond to work location codes 1 to 5, 10 to 12, 13, 20 to 
24, and 25, respectively. Different information is obtained by changing the 
location codes included in each group, restricting the analyses to specific 
year(s), examining data for a different contaminant, examining data for 
another industry or group of industries, or using different computer programs 
to compute different statistics for the same set of data. Table 3 illustrates 
how the same sandstone respirable quartz dust data appear when summarized by 
calendar year. 



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The organization of data and the selection of the computer program are 
options which MIDAS gives to the user. If the initial choice of data or pro- 
gram does not clearly answer the question, further analysis is possible. 

APPROACH 

The MSHA sampling data constitute the largest and most comprehensive set 
of environmental industrial hygiene data available from noncoal mines, but no 
system was previously available for data manipulation, analysis, and report- 
ing. The Bureau with cooperation from MSHA developed MIDAS to solve this 
problem. The data reduction and reporting programs in MIDAS reflect the pri- 
orities established by the Bureau and MSHA, and they will be further developed 
to reflect the changing needs of the Bureau, MSHA, and other agencies. A com- 
plete description of these programs is found in the User's Manual.^ 

The conceptual approach of MIDAS was to start with the entire data set 
and count the number of records within the code categories as shown in the 
tables A-1 to A-5. Once knowledge was gained about the number of records in 
these categories, decisions were made on how to edit records for particular 
contaminants, and priorities were assigned for the depth and sequence of anal- 
ysis for each contaminant. Among the personal samples, dust, noise, and weld- 
ing fumes received the highest priority, based upon the perceived health risks 
and the large body of records available. 

Two fundamental approaches to data analysis are available within MIDAS. 
The first involves starting with a large set of data and reducing it to 
smaller and smaller subsets. This step-by-step analysis produces a series of 
results that document contaminant levels found in industries and operations 
(occupations). Examination of data by calendar year allows trends in concen- 
trations and overexposures to be recognized within industries and occupations. 
The second approach identifies a particular mine, operation, industry, or 
other variable and selects individual records for analysis. MIDAS will select 
records, create a data file, and summarize the findings using the computer 
programs chosen by the investigator. Each of these approaches has been used 
to produce reports for investigators in the Bureau, MSHA, and NIOSH. However, 
there exists a limit to the conclusions that can be drawn from the available 
data. 

Limitations and Uses 

The Federal Mine Safety and Health Act of 1977 (Public Law 91-173) 
requires MSHA to enforce the safety and health regulations specified in the 
Code of Federal Regulations for coal and noncoal mines. ^ 

To insure compliance, samples are collected during mine inspections. 
The method in which miners are selected for sampling presents a statistical 

^Jahsman, W. E. , R. L. Johnson, D. J. Donaven, and W. F. Watts. MIDAS User's 
Manual, 1981. Available from Division of Automatic Data Processing, Bureau 
of Mines, Denver, Colo. 80225. 

"^U.S. Congress. The Federal Mine Safety and Health Act of 1977. Public 
Law 91-173, as amended by Public Law 95-164, 83 Stat. 803. 



problem, because the sampling strategy is judgmental and depends upon the MSHA 
inspector. MSHA inspectors are instructed to sample employees known or sus- 
pected to be at highest risk within each occupational category. Even more 
important, occupations and locations where overexposures occur are resampled, 
often several times, whereas other occupations and locations may not be sam- 
pled as often. However, it cannot be concluded that the data are systemati- 
cally biased upward; in fact, the degree and direction of the bias is unknown, 
and therefore assessment of the sampling error is difficult, since the sample 
may not reflect the whole population. Two mitigating factors are that 
(1) certain operations and locations are sampled so heavily that MSHA has 
a census and (2) MSHA inspectors, using common sense and judgment, have 
attempted to take "representative" samples within each occupation and loca- 
tion at each mine or mill. 

An example of MSHA sampling strategy is the loading, hauling, dumping, 
diesel occupation which was sampled 44,719 times for all contaminants 
(table A-3). Further breakdown shows that 9,490 of these were respirable 
quartz dust samples, which had 8.19 pet overexposures (table A-17), and an 
additional 10,576 noise dosimeter samples were collected on these operators 
with 41-pct overexposure. Clearly MSHA inspectors simultaneously sample load- 
haul-dump diesel operators for dust and noise. This is further shown by 
matching load -haul -dump diesel operators' social security numbers with dates 
of sample collection for these contaminants. This information is available in 
MIDAS . 

The result of a nonrandom sampling strategy is that statistical infer- 
ences based on models assuming randomness, known distribution, or homogeneity 
of variance must be made with caution. This report does not rely heavily upon 
statistical tests, although they are available in MIDAS; instead this report 
relies upon basic descriptive statistics to determine the trends in the MSHA 
dust data. A search is underway to identify a set of randomly collected dust 
data to act as a control for comparison with a matched set of randomly 
selected data from MIDAS. Such analysis may indicate the nature and direc- 
tion of the bias in the MSHA data. 

Pinpointing the precise nature of a problem is limited to the information 
contained in the sample record. Thus, although these data indicate that bag- 
ging had the greatest percentage of overexposure in 1978 and 1979, they do not 
identify the exact cause of the problem, because of the generalized coding 
system and because personal exposure samples are collected on miners who move 
freely within the mine or mill. No Indication of the characteristics of the 
material being bagged are given in the MSHA record, nor is there a description 
of the type of equipment. 

Another limiting factor is the variability in the number of samples 
collected for a given contaminant within a particular industry or operation. 
As shown in tables A-1 through A-4, the code categories have a wide range of 
personal exposure samples available for analysis. The diesel load-haul- 
dump operation in table A-3 has nearly 45,000 records of personal exposure, 
whereas backfilling has only 28 records. A likely explanation for this 
difference is that backfilling is rarely practiced in underground metal and 
nonmetal mines, whereas operating diesel-powered load-haul-dump vehicles is 
common in all types of mining and is therefore heavily sampled. Similarly, 



some contaminants are rarely sampled while others are sampled often, reflect- 
ing their relative pervasiveness and MSHA's priorities. 

MIDAS provides exact knowledge of what data are available, and its most 
obvious application is to help MSHA evaluate and plan its metal and nonmetal 
inspection program. Knowledge of past contaminant trends allows it to empha- 
size certain areas. NIOSH and the Bureau have a convenient system for ana- 
lyzing retrospectively this large body of data. Knowledge derived from this 
analysis will allow researchers in both agencies to better plan, explain, con- 
duct, and evaluate future research projects. 

Data Editing 

All dust and fiber records contained in MIDAS have been edited for 
obvious errors using the threshold limit value (TLV) as the reference. The 
TLV's for respirable quartz dust and total silica dust are determined by the 
formulas given below, and illustrate the reasoning behind the edit format. 

TLV for respirable quartz dust in mg/m^ is 

10 mg/m^ 



pet respirable quartz + 2 * 

and TLV for total silica dust in mg/m^ is 

30 mg/m^ 
pet quartz + 3 ' 

where the quartz content is greater than or equal to 1 pet. By substitution 
it can be determined that the TLV for respirable quartz dust must be between 
0.10 mg/m^ and 3.33 mg/m^, and that the TLV for total silica dust must be 
between 0.29 mg/m^ and 7.50 mg/m^. These values are derived by substituting 
1.0 pet and 100 pet for the percent quartz in the formulas. When a TLV fell 
within these ranges, the record was considered acceptable; a TLV figure out- 
side these ranges required an alternative explanation. The inspector has one 
code each for respirable quartz dust, tridymite, and cristobalite regardless 
of quartz content, so MIDAS created a code for respirable nuisance dust when 
the TLV listed for these contaminants indicated less than 1 pet quartz. Two 
codes are available for total dust samples, one for silica dust and one for 
nuisance dust. MSHA inspectors sometimes confuse these codes, so MIDAS cor- 
rectly reclassified these records based upon the TLV. 

Other common errors were recording the action level^ rather than the TLV, 
failure to record a new TLV after it changed, misplaced decimals, or missing 
TLV. The edit program did not change the concentration reported for any of 
the eight dust contaminants, and excessively high concentrations are being 
checked for possible editing. Table 4 outlines the editing rules for the dust 
contaminants, and table 5 summarizes the effects of editing the records. 

°The action level is defined as the exposure limit plus a 20-pct factor for 
dust-sampling error. 



TABLE 4. - Editing rules for MSHA dust data 

Resplrable quartz dust, mg/ra^: 

0.10 <^ TLV £ 3.33 Data OK. 

3.34 _< TLV _< 5.00 Change code to 02 (resplrable 

nuisance dust); change TLV to 5.0. 

TLV = 6 Change code to 02; change TLV to 5.0. 

5 < TLV < 6 Change code to 34 (total silica dust), 

6 < TLV _< 7.5 Change code to 34. 

TLV < 7.5 2 10 Change code to 04 (total nuisance 

dust); change TLV to 10. 
Reject other values. 

Talc, mppcf (million particles 
per cubic foot of air): 

TLV = 20,40 Data OK. 

Reject other values. 

Nuisance dust, mg/ra^: 

TLV = 10 Data OK. 

TLV = 12 Change TLV to 10. 

0.29 _< TLV < 7.50 Change code to 34. 

7.51 _< TLV _< 9.99 Change TLV to 10. 

Reject other values. 

Cristobalite, mg/ra^: 

0.05 _< TLV £ 1.67 Data OK. 

1.68 _< TLV _< 2.5 Change to code 02; change TLV to 5. 

Reject other values. 

Tridymite, mg/m^: 

0.05 _< TLV _< 1.67 Data OK. 

1.68 _< TLV _< 2.5 Change code to 02; change TLV to 5. 

Reject other values. 

Asbestos, fibers/cm^: 
1974 to 1978: 

TLV = 5,10 Data OK. 

TLV = 6 Change TLV to 5. 

1979 to present: 

TLV = 2,10 Data OK. 

Reject other values. 

Total silica dust, mg/m^: 

0.29 _< TLV £ 7.50 Data OK. 

7.51 _< TLV _< 10.00 Change code to 04; change TLV to 10. 

TLV = 12 Change code to 04; change TLV to 10. 

Reject other values. 



NOTE, — Code 02 was created during the edit for resplrable nuisance dust. The 
second TLV listed for talc and asbestos is for short-term samples. 



10 



TABLE 5. - Effect of editing contaminant records 



Contaminant 


Number of records 
before edit 


Number of records 
after edit 


Change, pet 


Respirable quartz •••••••••• 


42,865 



195 

9,814 

134 

7 

842 

7,153 


33,260 

7,904 

183 

8,874 

117 

3 

828 

8,163 


22.41 


Respirable nuisance dust... 
Talc fiber 


6.15 


Total nuisance dust 

Cristobalite 


9.58 
12.69 


Tridymite 


57.14 


Asbestos fiber. ............ 


1.66 


Total silica dust 


-14.11 






Total 


61,010 


59,332 


2.75 







NOTE. — Respirable nuisance dust was created during the edit of contaminants 
01, 11, and 12 (respirable quartz dust, cristobalite, and tridymite). 

The total effect of editing was to reduce the number of records by 
2.75 pet and to alter the TLV and/or the contaminant code in about 18 pet of 
the records. 

Statistical Programs 

MIDAS programs are completely described in the User's Manual. These pro- 
grams, which provide basic descriptive statistics that summarize the data file 
created by the user, include the following parameters: Mean, median, range, 
standard deviation, variance, the number and percent of samples where the con- 
centration equals or exceeds the TLV, and the number and percent of samples 
where the concentration exceeds the TLV by 1,2 ar more. Other programs 
provide cumulative frequency distributions for concentration and the 
concentration-TLV ratio. MIDAS has access to the Biomedical Series of pro- 
grams developed at UCLA, which include graphics and higher level statistical 
programs such as regression analysis. ^ 

INITIAL RESULTS OF DUST ANALYSIS 

Summaries for respirable quartz dust, total nuisance dust, asbestos 
fiber, and total silica dust appear in table 6. During 1975-79 the respirable 
dust category exhibited a drop from 28.34 to 14.09 pet in the proportion of 
samples that were greater than or equal to the TLV (GETLV). The same downward 
trend is observed for the other contaminants with the exception of asbestos, 
which had fewer samples collected and whose TLV was lowered in 1979 from 
5 fibers/cm^ to 2 fibers/cm^. The average concentration figures, especially 
for nuisance dust and asbestos fiber, fluctuate from year to year owing to a 
few exceptionally high concentrations, as reflected in the high standard 
deviations. 



^Dixon, W. J., and M. B. Brown (eds). BMDP-79 Biomedical Computer Programs 
P-Series. University of California Press, Los Angeles, Calif,, 1979, 
880 pp. 



11 



TABLE 6. - Summary statistics for respirable quartz dust, total nuisance dust, 

asbestos fiber, and total silica dust 



Yearl 



Total 
samples 



GETLV , 
pet 



1.2xTLV,2 
pet 



Concentration 



Average Median 



Standard 
deviation 



Average 
C/TLV3 



Average 

silica, 

pet 



RESPIRABLE QUARTZ DUST 



1974.. 


335 


48.66 


42.69 


0.92 


0.54 


1.08 


1.88 


20.99 


1975.. 


2,710 


28.34 


23.95 


.93 


.47 


1.79 


1.31 


12.53 


1976.. 


6,314 


25.75 


21.30 


1.08 


.48 


5.10 


1.32 


9.93 


1977.. 


7,745 


19.47 


15.73 


.98 


.43 


6.05 


1.17 


9.51 


1978.. 


8,285 


16.37 


13.20 


.83 


.38 


2.27 


.84 


9.81 


1979.. 


7,779 


14.09 


11.17 


.93 


.34 


4.61 


1.11 


9.09 


1980.. 


92 


43.48 


35.87 


.80 


.58 


.72 


1.35 


16.08 









TOTAL 


NUISANCE 


DUST 








1974.. 


71 


23.94 


14.08 


8.23 


4.14 


13.84 


0.82 


NAp 


1975.. 


473 


37.84 


33.62 


16.61 


6.56 


26.40 


1.66 


NAp 


1976.. 


1,313 


31.23 


27.57 


12.72 


4.37 


23.70 


1.27 


NAp 


1977.. 


1,615 


33.99 


29.60 


13.83 


5.64 


28.40 


1.38 


NAp 


1978.. 


2,181 


24.94 


21.23 


10.22 


4.08 


20.85 


1.02 


NAp 


1979.. 


3,150 


20.48 


16.76 


8.18 


3.02 


21.66 


.82 


NAp 


1980.. 


71 


14.08 


12.68 


11.53 


.22 


51.40 


1.15 


NAp 



ASBESTOS FIBER 



1974.. 


14 


42.86 


42.86 


33.18 


3.87 


53.50 


6.64 


NAp 


1975.. 


114 


2.63 


2.63 


1.37 


.88 


1.93 


.27 


NAp 


1976.. 


241 


8.30 


7.47 


2.09 


.70 


3.82 


.42 


NAp 


1977.. 


165 


5.45 


4.24 


1.03 


.30 


1.89 


.19 


NAp 


1978.. 


68 


2.94 


1.47 


1.07 


.62 


1.26 


.21 


NAp 


1979.. 


221 


13.57 


9.50 


1.04 


.44 


1.70 


.52 


NAp 


1980.. 


5 


60.00 


40.00 


2.28 


2.20 


.53 


1.14 


NAp 









TOTAL 


SILICA ] 


OUST 








1974.. 


34 


41.18 


29.41 


4.73 


1.65 


7.40 


1.60 


13.57 


1975.. 


197 


67.01 


62.44 


15.49 


8.28 


23.93 


3.74 


5.60 


1976.. 


785 


42.29 


36.69 


7.88 


3.13 


15.42 


1.98 


5.62 


1977.. 


1,640 


39.02 


35.42 


8.68 


2.69 


23.00 


2.68 


6.10 


1978.. 


1,757 


21.86 


18.50 


4.67 


1.45 


12.70 


1.39 


6.40 


1979.. 


3,725 


19.89 


16.21 


3.73 


1.31 


9.58 


1.04 


6.87 


1980.. 


25 


32.00 


28.00 


5.79 


1.99 


8.09 


1.08 


3.08 



NAp Not applicable. 
^Data recorded for 1975-1 
only a small segment o 
^Percent of records with 
^Average concentratlon-to 



979 are complete; those for 1974 and 
f the year. 

concentrations exceeding the TLV by 
-TLV ratio. 



1980 represent 
20 pet. 



Dust data generally follow a log normal distribution, and the MSHA data 
are no exception. Figures A-1 to A-6 show the effect of a logarithmic trans- 
formation on respirable quartz, total nuisance, and total silica dust. In 
each case the transformed data are a straight line with the exception of the 
upper and lower tails, which remain slightly skewed. The transformations 
reveal the underlying normal distribution and lessen the effects of the 
extremes upon the mean, standard deviation, and variance. 



12 



The yearly respirable quartz dust data, broken down by industry and loca- 
tion, are shown in tables A-6 to A-10; table A-11 shows the totals for all 
data between 1974 and 1980. These tables show the percentage of overexposures 
for respirable quartz dust in the metal nonmetal industries; for example, for 
1979 (table A-10) samples from surface mills have 26.46 pet GETLV and sand- 
stone (38.37 pet), clay and shale (31.84 pet), sand and gravel (31.69 pet), 
and other nonmetals (76.19 pet) account for a major share of those overexpo- 
sures. These industries accounted for 641 of 1,266 samples collected in 1979, 
and 243 of those samples (37.91 pet) equaled or exceeded the TLV. Referring 
back to table 2, which summarizes all respirable quartz data for sandstone by 
operation and location, many operations in the mill are very dusty, most nota- 
ble bagging and sizing. It is possible to break table 2 down by years or 
groups of years, but this involves fewer samples in each category and makes 
the information less reliable. However, individual sample records can be 
checked with respect to specific mills; MIDAS allows this precision. 

Yearly summaries of respirable quartz dust by operation and location are 
in tables A-12 to A-17. Table 7 ranks the dustier operations and is based 
upon data in tables A-15 and A-16. MIDAS will provide similar analyses for 
any contaminant. Tables 8 and 9 rank the dusty operations according to over- 
exposure to total nuisance and total silica dust. Of the 40 MSHA occupation 
codes, baggers have the greatest percentage of overexposure with over 40 pet 
(GETLV) of the 1,536 samples for samples collected in 1978-79. The other 
seven occupations ranked in table 7 also appear in tables 8 and 9, showing 
that they are consistently dusty regardless of contaminant sampled. 

TABLE 7. - MSHA respirable quartz dust samples for 1978-79 

for eight dusty operations 



Operation 



1978 



Number of 
samples 



GETLV, 
pet 



1979 



Number of 
samples 



GETLV, 
pet 



Bagger , 

General labor , 

Grinding , 

Drying, filtering, and thickening...., 

Percussive drilling , 

Crushing , 

Sizing , 

Rotary drilling , 

Total or average, all operations, 



301 
760 
284 
202 
438 
1,357 
319 
166 



41.86 
25.92 
25.00 
21.29 
21.23 
20.78 
17.55 
17,47 



302 
867 
155 
220 
295 
1,392 
299 
183 



41.72 
23.07 
18.71 
16.82 
22,37 
17.60 
17.39 
19.67 



8,285 



16,37 



7,779 



14,09 



13 



TABLE 8. - MSHA nuisance dust samples for 1978-79 
for 12 dusty operations 



Operation 



1978 



Number of 
samples 



GETLV, 
pet 



1979 



Number of 
samples 



GETLV, 
pet 



Bagger 

Sizing 

Rotary driller 

Grinding 

Mining machine operator 

Drying, filtering, and thickening 

General labor 

Loading, hauling, and dumping — electric 

Percussive drilling 

Supply handling 

Mechanic. 

Crushing 

Total or average, all operations.. 



245 

118 
86 

145 
40 
96 

274 
62 
52 
64 
91 

157 



46.53 
45.76 
38.37 
37.93 
35.00 
29.17 
27.01 
22.58 
21.15 
17.19 
15.38 
14.01 



429 
92 
83 

126 
30 

171 

366 
81 

129 
66 
79 

339 



40.33 
33.70 
24.10 
25.40 
60.00 
19.88 
31.69 
17.28 
15.50 
24.24 
22.78 
18.88 



2,181 



24.94 



3,150 



20.48 



TABLE 9. - MSHA total silica dust samples for 1978-79 

for nine dusty operations 



Operation 



1978 



Number of 
samples 



GETLV, 
pet 



1979 



Number of 
samples 



GETLV, 
pet 



Bagger 

Grinding 

Rotary drilling 

General labor 

Drying, filtering, and thickening 

Percussive drilling. 

Crushing 

Mechanic 

Sizing 

Total or average, all operations. 



106 
55 
45 

138 
33 
60 

244 
75 
58 



50.00 
41.82 
35.56 
28.26 
27.27 
26.67 
25.00 
22.67 
21.86 



153 
64 
75 

324 
57 

125 

635 
68 

138 



54.90 
34.38 
37.33 
24.38 
24.56 
21.60 
28.50 
23.53 
35.51 



1,757 



21.86 



3,725 



19.89 



This brief analysis demonstrates how MIDAS can rank occupations for dust 
overexposure and identify the industries where they are most likely to occur. 
MSHA inspectors are finding a lower percentage of overexposures, and bagging, 
grinding, and general labor cleanup operations are more likely to have concen- 
trations that exceed the exposure limit. Research is underway to determine if 
the reduction in the percentage of overexposures is a real difference due to 
lower dust levels or is merely due to alterations in inspectors' sampling 
strategy. Preliminary statistical evidence suggests that sampling strategy 
is not responsible for the reduction. 

MIDAS is programed to calculate cumulative frequency distributions for 
concentration and the concentration-to-TLV ratio. Figures 1 and 2 show log 
probability plots for the cumulative frequencies of the three dust contam- 
inants most often sampled. Concentrations for respirable quartz dust are 
lower than those for the other two, because collection of respirable quartz 



14 



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15 



dust requires a cyclone preseparator to remove large particles from the 
alrstream before it reaches the filter. This is particularly evident in fig- 
ure 2, where about 80 pet of the respirable quartz dust samples have con- 
centrations of 1.0 mg/ra^ or less, as opposed to far lower percentages for 
total nuisance and total silica dust. That figure also shows an interesting 
difference in the distribution of total silica and total nuisance dust concen- 
trations. A sample containing less than 1 pet silica is classified as nui- 
sance dust, while one with more than 1 pet is classified as total silica dust. 
Fifty percent of the total silica dust concentrations are greater than 
1.1 mg/m^, whereas 70 pet of the total nuisance dust concentrations are above 
that level. Further analysis of the nuisance dust concentrations shows that 
MSHA collected 8,874 samples from 1974 through 1980, with 2,354 (26.53 pet) 
exceeding the 10 mg/m^ TLV. Three industries, salt, potash, and cement, 
accounted for 923 (39 pet) of all nuisance dust overexposures. None of these 
industries uses substantial amounts of water to control dust because the prod- 
uct is water soluble. Furthermore, MSHA has a policy of emphasizing dust con- 
trol in mines and mills known to have silica dust. These two factors may 
account for the lower total silica dust concentrations. Table 6 shows the 
yearly average concentration for both contaminants, and for each year the 
average concentration for total nuisance dust is higher. 

Fewer respirable quartz dust samples exceeded the TLV in 1979 than in 
1975-76. In 1979 approximately 12 pet more samples had a concent rat ion-TLV 
ratio of 1.0 or less when compared with 1975-76 data (fig. 3). T-test results 
on the log means for the two sets of data in figure 4 were significantly dif- 
ferent (p = 0.001). On the other hand 14 pet of the 1979 samples still had 
concentrations at or above the TLV (fig. 4). More than 21 pet of the nuisance 
dust samples collected in 1979 had concentrations GETLV of 10 mg/m^ (fig* 5). 

Table 10 summarizes employment figures for the 20 largest metal and non- 
metal industries and shows the percentage of workers employed in underground 
mines, sand and gravel operations, stone quarries, open pits, and mills. It 
also helps determine which industries are dustier than others. Industries 
with 30 pet or more of their dust samples at or above the TLV include pot- 
ash (50.65 pet), salt (40.35 pet), other nonmetals (35.48 pet), sandstone 
(31.64 pet), molybdenum (31.17 pet), and clay and shale (30.41 pet). These 
are well above the 6-year industry average of 19.59 pet (table 10). 



16 



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10 



FIGURE 5. - Log probability plot for total nuisance dust concentration-TLV ratios for 1975- 
76 and 1979. 



19 



Examination of outlier values arbitrarily defined as concentrations 
>50 mg/ra^ (table 10) has shown them not to be randomly distributed. Inter- 
estingly, the industries with the worst nuisance dust problems, salt, cement, 
and potash, account for a far higher percent of the outliers than would be 
expected from their share of the total samples. Salt accounts for 1.5 pet of 
the samples but 17.4 pet of the outliers, cement for 9 pet of the samples but 
13.8 pet of the outliers, and potash for 0.5 pet of the samples but 4.5 pet of 
the outliers. In this instance, examination of outlier values would have 
identified three of the dustier industries. 

MIDAS has provided reports to a number of agencies, including MSHA and 
NIOSH, and to private consultants and industry representatives. MIDAS summa- 
rized the records collected on welders and calculated the additive exposure 
for welders. MIDAS analyzed samples of lead exposure and provided a summary, 
which was used as background for congressional testimony on proposed changes 
to the lead standard. Similar summary analyses were done for asbestos fiber, 
respirable quartz dust, total silica dust, and noise. However, the system's 
full potential has not been exploited. It is possible for many people to gain 
direct access to MIDAS and use the system with a minimum amount of training. 
Such activity would greatly expand the value of MIDAS by addressing a wide 
variety of questions at the same time. Utilizing MIDAS in this way would ful- 
fill the system's potential. Multiple use is easily established at low cost, 

SUMMARY 

The Mine Inspection Data Analysis System (MIDAS) has been developed to 
sort, analyze, summarize, and report MSHA sampling data for metal and nonmetal 
health samples. Information provided by MIDAS can assist to plan, rank, eval- 
uate, and conduct health research projects. The system contains the single 
largest source of industrial hygiene data for noncoal mining. 

Results indicate that certain mining operations are dustier than others 
and that the three dustiest are bagging, grinding, and general labor cleanup. 
Six industries are substantially dustier than the others: sandstone, molyb- 
denum, salt, potash, clay and shale, and other nonmetals. Operations con- 
ducted in a mill or surface plant are likely to be dustier than operations 
conducted in underground or open pit mines, 

MIDAS can be used directly by many agencies, consultants, or operators, 
and it is hoped that such direct access will be realized in the future. 



20 



APPENDIX 



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CONCENTRATION, mg/m^ 

FIGURE A-1. - Probability plot of respirable quartz dust data, 1974-80 



26 



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FIGURE A-2. - Log normal probability plot of respirable quartz dust data, 1974-80. 



22 



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175 



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FIGURE A-4. - Log normal probability plot of total nuisance dust data, 1974-80. 



2.4 



24 



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FIGURE A-5. - Probability plot of total silica dust data, 1974-80. 



75 



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LOG CONCENTRATION, mg/m3 

Log normal probability plot of total silica dust data, 1974-80. 



2.4 



26 



TABLE A-1. - MSHA industry codes for metal and nonmetal mines 



Code 



Industry 



Code 



Industry 



Code 



Industry 



STONE QUARRIES AND MILLS 



01 


Cement 


04 


Lime 


07 


Slate 




02 


Granite 


05 


Marble 


08 


Traprock 




03 


Limestone 


06 


Sandstone 


09 


Miscellaneous 


stone 







METAL 


MINES AND MILLS 






10 


Antimony 


14 


Copper 


19 


Molybdenum 


11 


Bauxite (includ- 


15 


Gold-silver, 


20 


Titanium 




ing alumina mills) 




lode and placer 


21 


Tungsten 


12 


Beryl 


16 


Iron 


22 


Uranium 


13 


Chromite 


17 


Lead-zinc 


23 


Mercury 






18 


Manganese 


29 


Other metals 



NONMETAL MINES AND MILLS 



40 


Asbestos 


46 


Gypsum 


53 


Sodium compounds 


41 


Barite 


47 


Magnesite 


54 


Sulfur 


42 


Boron minerals 


48 


Mica 


55 


Talc, soapstone. 


43 


Clay and shale 


49 


Phosphate rock 




and pyrophyllite 


44 


Feldspar 


50 


Potash 


56 


Gilsonite 


45 


Fluorspar 


51 


Pumice 


58 


Oil and/or shale 






52 


Salt 


59 


Other nonmetals 



MISCELLANEOUS NONFUEL MINES AND MILLS 



60 



Sand and gravel 



NOTE, — Other metals and other nonmetals are defined in the Standard Industrial 
Classification (SIC) Codes published by the National Bureau of 
Standards, 1979, Washington, D.C. 

TABLE A-2. - MSHA mine location codes for metal and nonmetal mines 



Code 



Mine location 

Underground metal mine , 

Underground nonmetal mine , 

Underground stone mine , 

Underground shop 

Underground mill , 

Open pit metal , 

Open pit nonmetal mine. , 

Open pit crushed stone , 

Sand and grave 1 

Surface, shop 

Surface, crushing , 

Surface, grinding , 

Surface, flotation and reagents , 

Surface, miscellaneous , 

Surface, mill (bagging, screening, etc.), 



Number of personal samples 



01 
02 
03 
04 
05 
10 
11 
12 
13 
20 
21 
22 
23 
24 
25 



9,741 

6,617 

2,060 

327 

285 

4,254 

9,802 

36,015 

19,453 

7,000 

12,318 

5,070 

2,138 

11,340 

19,399 



27 



TABLE A-3. - MSHA operation codes for metal and nonmetal mines 



Code 



Operation 



Number 

of 

personal 

samples 



Code 



Operation 



Number 
of 
personal 
samples 



01 
02 
03 
04 
05 
06 
07 
08 
09 
10 
11 
12 

13 

14 

15 

16 
17 
18 
19 



Slushing 

Machine mucking. 

Hand mucking 

Timbering 

Rock bolting 

Backfilling 

Blasting 

Rock sawing 

Drilling, percussive.... 

Drilling, rotary 

Drilling, diamond 

Loading, hauling, dump- 
ing — electric. 

Loading, hauling, dump- 
ing — diesel. 

Loading, hauling, dump- 
ing — gasoline. 

Loading, hauling, dump- 
ing — compressed air. 

Mining machine operator. 

Track crew 

Complete mining cycle... 

Concrete operations 



589 

409 

209 

386 

331 

28 

484 

712 

5,540 

2,998 

293 

3,249 

44,719 

1,630 

673 

576 

150 

3,025 

548 



20 
21 
22 
23 

24 
25 
26 
27 
28 
29 
30 

31 
32 
33 
34 
35 
36 
37 
38 
39 
40 



Hoisting 

Bulldozing 

Slurry 

General labor and 
cleanup. , 

General shopwork,... 

Welding 

Mechanic. 

Crushing 

Grinding 

Roasting, retorting. 

Drying, filtering, 
and thickening. 

Sizing 

Concentrating 

Chemical operations. 

Supply handling 

Technical services.. 

Administration 

Bagger 

Pelletizing. 

Dredging 

Jet piercing 



638 

3,774 

238 

13,299 
1,766 
8,342 
4,989 

17,698 
4,640 
1,443 
3,624 

5,080 
1,412 

485 
1,411 

875 
1,479 
6,026 

652 
1,313 

188 



28 



TABLE A-4, - MSHA contaminant codes for personal samples and the number 

of records for each contaminant 



Type of measurement 



Unit of measurement 
mg/m^ 
mppcf 
mppcf 
mg/m^ 
mppcf 
mppcf 
mppcf 
mppcf 
mppcf 
mppcf 
mg/m^ 
mg/m^ 
mg/m^ 
mg/m^ 
mg/m^ 
yg/m^ 



Number of records 



Respirable quartz dust 

Midget impinger, quartz dust 

Talc, nonasbestiform. 

Nuisance dust, total particulate. 

Mica 

Perlite 

Soapstone 

Diatomaceous earth 

Tremolite 

Graphite. 

Cristobalite, respirable. 

Tridymite, respirable. 

Mercury vapor 

Lead, total particulate 

Cadmium, total particulate 

Arsenic and compounds, total 

particulate. 

Manganese, total particulate 

Beryllium, total particulate 

Iron oxide, total particulate.... 
Asbestos, fibers greater than 5vim 

Cobalt, total particulate 

Copper fume, total particulate... 
Molybdenum, total particulate. . . . 

Nickel, total particulate 

Platinum, total particulate 

Tungsten, total particulate 

Vanadium fume, total particulate. 
Zinc oxide fume, total 

particulate. 

Chromium, total particulate 

Oil mist, total particulate 

Diesel fumes 

Cyanides, total particulate 

Total airborne silica dust 

Welding fume, total particulate.. 

Mercury compounds 

Noise, dosimeter measurement 

Sound level meter measurement.... 
Magnesium oxides, total 

particulate. 
Aluminum oxides, total 

particulate. 
Titanium oxides, total 

particulate. 



Ug/m^ 

mg/m^ 
fibers/cm^ 
mg/m^ 
mg/m^ 
mg/m^ 
rag/m^ 

mg/m^ 
yg/m^ 
mg/m^ 

mg/m^ 
mg/m^ 
mg/m^ 
mg/m^ 
mg/m^ 
mg/m^ 
mg/m^ 

pet 

dBA 
mg/m^ 



mg/ 



m- 



mg/m- 



42,865 

30 

195 

9,814 

11 

11 

1 

31 

14 

25 

134 

7 

67 

828 

276 

710 

865 
224 
836 
842 
399 
488 
282 
428 
7 
7 
505 
478 

452 
50 
3 
2 
7,153 
573 

73,247 
1,611 
400 

498 

500 



NOTE. — After the data were edited, code 02 was reassigned to respirable 

nuisance dust and the midget impinger records were saved on a backup 
tape. 



29 



TABLE A-5. - MSHA contaminant codes for area samples and the number 

of records for each contaminant 



Code 



Type of measurment 

Mercury vapor 

Radon daughter measurement, 

Nitrogen oxide , NO 

Nitrogen dioxide, NO 2 ■ 

Nitrogen oxides , NOx 

Carbon monoxide, CO 

Carbon dioxide, CO 2 

Aldehydes , 

Ammonia, NH3 , 

Hydrogen sulfide, H2S , 

Sulfur dioxide , SO 2 < 

Chlorine, CI2 ■ 

Sulfuric acid mist 

Hydrogen cyanide, HCN , 

Carbon disulfide, CS2 

Perchlorethylene 

Phosgene 

Oxygen 

Hydrocarbons , total 

Methane 



Unit of measurement 



Number of records 



13 
50 
70 
71 
72 
73 
74 
75 
67 
77 
78 
79 
80 
87 
88 
89 
90 
91 
92 
93 



mg/m^ 
WL 
ppm 
ppm 
ppm 
ppm 
ppm 
ppm 
ppm 
ppm 
ppm 
ppm 

mg/m^ 
ppm 
ppm 
ppm 
ppm 
pet 
ppm 
pet 



688 

15,736 

107 

6,160 

262 

17,223 

14,582 

109 

144 

1,615 

354 

15 

1 

111 

3 

9 

14 

9,435 

88 

13,431 



NOTE. — WL is working level. 



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